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Censored Quantile Regression Redux

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  • Roger Koenker
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    Abstract

    Quantile regression for censored survival (duration) data offers a more flexible alternative to the Cox proportional hazard model for some applications. We describe three estimation methods for such applications that have been recently incorporated into the R package quantreg: the Powell (1986) estimator for fixed censoring, and two methods for random censoring, one introduced by Portnoy (2003), and the other by Peng and Huang (2008). The Portnoy and Peng-Huang estimators can be viewed, respectively, as generalizations to regression of the Kaplan-Meier and Nelson-Aalen estimators of univariate quantiles for censored observations. Some asymptotic and simulation comparisons are made to highlight advantages and disadvantages of the three methods.

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    File URL: http://www.jstatsoft.org/v27/i06/paper
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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 27 ()
    Issue (Month): i06 ()
    Pages:

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    Handle: RePEc:jss:jstsof:27:i06

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    References

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    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, October.
    2. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
    3. Fitzenberger, Bernd & Winker, Peter, 2007. "Improving the computation of censored quantile regressions," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 88-108, September.
    4. Lindgren, Anna, 1997. "Quantile regression with censored data using generalized L1 minimization," Computational Statistics & Data Analysis, Elsevier, vol. 23(4), pages 509-524, February.
    5. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    6. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
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    Cited by:
    1. Achim Zeileis & Roger Koenker, . "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, American Statistical Association, vol. 27(i01).
    2. Wocken, Meike & Kneib, Thomas, 2012. "Tobit regression to estimate impact of EU market intervention in dairy sector," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122528, European Association of Agricultural Economists.
    3. Peng, Limin, 2012. "Self-consistent estimation of censored quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 368-379.
    4. Yanlin Tang & Huixia Wang & Xuming He & Zhongyi Zhu, 2012. "An informative subset-based estimator for censored quantile regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 21(4), pages 635-655, December.
    5. Schmillen, Achim & Möller, Joachim, 2012. "Distribution and determinants of lifetime unemployment," Labour Economics, Elsevier, vol. 19(1), pages 33-47.
    6. Polpo, A. & de Campos, C.P. & Sinha, D. & Lipsitz, S. & Lin, J., 2014. "Transform both sides model: A parametric approach," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 903-913.

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